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首页> 外文期刊>Tunnelling and underground space technology >Deep learning-based pixel-level rock fragment recognition during tunnel excavation using instance segmentation model
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Deep learning-based pixel-level rock fragment recognition during tunnel excavation using instance segmentation model

机译：基于深度学习的像素级摇滚片段识别在隧道挖掘过程中使用实例分割模型

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摘要

Timely recognition of rock fragments and their morphological sizes can allow adjustment of excavation parameters during tunnel boring machine (TBM) tunnelling. Traditional manual inspection strongly relies on the experience and subjective determination of the human operators, and sieving tests cannot be conducted in real time and are energy-consuming. Rock fragments in real-world images are frequently observed against a dark background, distributed with a high size diversity, and blocked by each other. A novel deep learning-based method is proposed in this paper to achieve real-time on-site rock fragment recognition. The proposed instance segmentation model comprises two subnetworks: object detection and semantic segmentation. The object detection subnetwork is designed based on a modified single-shot detector architecture, and multilevel feature fusion, prior anchors, and self-attention modules are utilised to localise rock fragment regions. The semantic segmentation subnetwork is designed based on U-net. Down-sampling stages use the structures of the object detection subnetwork to share the extracted features of the rock fragments, and up-sampling stages employ skip connection and self-attention modules to accomplish binary segmentation in each detected bounding box. A total of 50 original images with a resolution of 4096 & times; 3072 were collected: 35 for training and 15 for testing. The results showed that 88.8% of the rock fragments can be recognised and that the average recall and average intersection-over-union values reach 0.87 and 0.76, respectively. Small rock fragments inevitably missed in the labelling process and extremely large ones can also be recognised. The predicted size distributions of the rock fragments fit well with the ground truth ones. Ablation experiments were conducted to further demonstrate the effectiveness of the proposed method. This study presents both visual recognition and statistical results of the size distribution of rock fragments during TBM tunnelling, which can assist in the prediction of rock properties and the adjustment of excavation parameters.
机译：及时识别岩石碎片及其形态尺寸可以允许在隧道钻孔机（TBM）隧道期间调整挖掘参数。传统的手动检查强烈依赖于人工操作者的经验和主观测定，并且无法实时进行筛分测试，并且能耗。真实世界图像中的岩石片段经常观察到深色背景，分布在高尺寸的分集，并彼此阻挡。本文提出了一种新的基于深度学习的方法，实现了实时的现场岩石片段识别。所提出的实例分段模型包括两个子网：对象检测和语义分割。物体检测子网基于改进的单次检测器架构设计，以及多级特征融合，先前锚和自我关注模块用于本地化岩石片段区域。语义分割子网基于U-Net设计。下采样阶段使用对象检测子网的结构来共享摇滚片段的提取功能，并采样阶段采用跳过连接和自我注意模块来完成每个检测到的边界框中的二进制分段。共有50个原始图像，分辨率为4096和时间;收集3072：35用于训练和15次进行测试。结果表明，可以识别出88.8％的岩石片段，并且平均召回和平均交叉口分别达到0.87和0.76。在标签过程中不可避免地错过的小岩石片段也可以识别出极大的岩石片段。岩石碎片的预测尺寸分布与地面真理较好。进行消融实验以进一步证明该方法的有效性。本研究介绍了在TBM隧道期间岩石碎片尺寸分布的视觉识别和统计结果，这可以有助于预测岩石性能和挖掘参数的调整。

著录项

  • 来源
    《Tunnelling and underground space technology》 |2021年第9期|104072.1-104072.15|共15页
  • 作者

    Qiao Weidong; Zhao Yufei; Xu Yang; Lei Yumeng; Wang Yujie; Yu Shu; Li Hui;

  • 作者单位

    Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China|Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China;

    China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River 20 Chegongzhuang West Rd Beijing 100048 Peoples R China;

    Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China|Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China;

    China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River 20 Chegongzhuang West Rd Beijing 100048 Peoples R China;

    China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River 20 Chegongzhuang West Rd Beijing 100048 Peoples R China;

    China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River 20 Chegongzhuang West Rd Beijing 100048 Peoples R China;

    Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China|Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    Rock Fragment Recognition; Instance Segmentation; Feature Fusion; Prior Anchors; Self-attention Mechanism; Tunnel Boring Machine Tunnelling;

    机译：岩石片段识别;实例分割;特征融合;现有锚;自我关注机制;隧道镗床隧道;

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